Show HN: Cortex – local-first encrypted memory for AI agents (Rust, MCP)

— zero install, 124KB WASM, runs entirely client-side.[🧠 Try Cortex in your browser]

If Cortex helps your AI remember,— it takes 1 second and helps others discover the project.[give it a ⭐]

Your AI's memory lives on your device — your data never leaves, never costs, never spies. Pure Rust. 3.8MB binary. No third-party servers in the data path, zero telemetry, zero cost. Syncs through your own cloud storage. (On-device semantic search downloads a ~30MB model once on first use, then runs fully offline — or go 100% offline with CORTEX_NO_EMBEDDINGS=1

. See Security & Privacy.)

Philosophy:Your memories are yours — not a cloud provider's training data, not a startup's monetization asset, not a government's surveillance target. Cortex runs 100% on your hardware, stores everything in your own database, and syncs only through your own cloud storage (iCloud, Google Drive, OneDrive, Dropbox). No middleman ever sees your data. No API key required. No account to create. Just plug it into your AI agent and it remembers — privately, permanently, and at sub-millisecond speed.

LLMs start blank every session. Your assistant forgets your name, your preferences, the conversation you had yesterday, the decision you made last week. Current "memory" solutions are flat text files, keyword grep, or cloud APIs that add 200-500ms latency, charge you for the privilege, and send your personal data to someone else's server.

Cortex fixes this. It gives your AI a structured, queryable, self-evolving long-term memory that persists across sessions, channels, and contexts — with Bayesian beliefs that self-correct, a people graph that resolves identities across platforms, and sub-millisecond performance on everything. All running locally, all yours.

Cortex	Mem0	OpenAI Memory
Privacy
100% local, zero cloud	Cloud API (your data on their servers)	OpenAI servers
Latency
156µs ingest, 568µs search
~200-500ms	~300-800ms
Cost
Free, forever	$99+/mo (Pro)	ChatGPT Plus ($20/mo)
Memory tiers
4 (Working/Episodic/Semantic/Procedural)	1 (flat)	1 (flat)
Bayesian beliefs
Self-correcting with evidence	No	No
People graph
Cross-channel identity resolution	Paid tier only	No
Conversation compression
Automatic session summarization	No	No
Relationship inference
Pattern-based (EN + CN)	No	No
Temporal retrieval
Intent-aware ("recently" / "first time")	No	No
Contradiction detection
Automatic with confidence scores	No	No
Consolidation
Episodic → Semantic auto-promotion	No	No
Context injection
Token-budgeted LLM-ready output	Manual	Automatic but opaque
Import/Export
Full JSON backup & restore	API only	No export
Self-hosted
Native binary, Docker, MCP	Cloud only	Cloud only
Binary size
3.8 MB	npm package	N/A
Dependencies
0 runtime services (single binary)	Node.js + cloud	N/A
Open source
MIT	Partial	No
Encryption
AES-256-GCM encrypted sync (opt-in)	No	No
Key rotation
Versioned envelopes, forward secrecy	No	No
Privacy levels
Private (default, never syncs) / Shared / Public — per-memory opt-in, demote retracts from other devices	No	No
Tool authorization
Deny-by-default capability policy on the MCP surface	No	No
Zero telemetry
No analytics, no phone-home, verifiable	Unknown	No
Cost
Free forever, unlimited	$99+/mo (Pro)	$20/mo (Plus)
Chinese NLP
Native (inference, retrieval, relationships)	No	Limited
Namespace isolation
Per-user/context memory separation	No	No
Plugin system
Compile-time hooks for ingest/retrieve/consolidation	No	No
MCP tools
30 tools for Claude/LLM integration	3rd party	N/A

Operation	Cortex	Mem0 (cloud)	File-based
Ingest	156µs
~200ms	~1ms
Search (top-10)	568µs
~300ms	~10ms
Context generation	621µs
~500ms	manual
Belief update	66µs
N/A	N/A
People graph	51µs
paid tier	N/A
Structured facts	45µs
N/A	N/A
1K memories search	1.6ms
~500ms	~50ms

528x faster than Mem0 cloud. With features neither Mem0 nor OpenAI Memory offer.

Note:Benchmarks include proactive inference (auto-extracting facts, preferences, relationships) on every ingest. Raw ingest without inference is ~15µs. Numbers fromcargo bench

on M-series Mac.

LoCoMo Benchmark (ACL 2024)

Academic-grade long-term conversation memory evaluation — 10 conversations, 1540 QA pairs across 4 categories.

System	Single-hop	Multi-hop	Open-domain	Temporal	Overall
Backboard	89.4%	75.0%	91.2%	91.9%	90.0%
MemMachine v0.2	—	—	—	—	84.9%
Cortex v1.7
72.5%
59.5%
88.8%
74.1%
73.7%
Mem0-Graph	65.7%	47.2%	75.7%	58.1%	68.4%
Mem0	67.1%	51.2%	72.9%	55.5%	66.9%
OpenAI Memory	—	—	—	—	52.9%

Key findings:

Open-domain 88.8%— leads Mem0 (72.9%) by +15.9%** Temporal 74.1%— leads Mem0 (55.5%) by +18.6% Single-hop 72.5%— leads Mem0 (67.1%) by +5.4% Multi-hop 59.5%— leads Mem0 (51.2%) by +8.3% Overall 73.7%**— beats Mem0 (66.9%) by +6.8%, beats OpenAI Memory (52.9%) by +20.8%

Cortex outperforms Mem0 on all 4 categories — while running 100% locally, end-to-end encrypted, at $0 cost.

Setup:Claude Sonnet 4 (QA + judge), nomic-embed-text (embeddings via Ollama), top-30 retrieval. Fully reproducible:python3 bench/locomo_bench.py

Cortex implements a 4-tier memory model inspired by human cognition:

                    +---------------------+
                    |   Working Memory    |  Current session context
                    +---------------------+
                              |
                    +---------------------+
                    |   Episodic Memory   |  Raw experiences: conversations, events, observations
                    +---------------------+
                              |  consolidation (decay, promotion, pattern extraction)
                    +---------------------+
                    |   Semantic Memory   |  Distilled facts, preferences, relationships
                    +---------------------+
                              |
                    +---------------------+
                    | Procedural Memory   |  Learned routines, user-specific workflows
                    +---------------------+

Working holds the current session scratch pad. Episodic stores raw experiences with timestamps and source metadata. The Consolidation Engine periodically promotes recurring patterns into Semantic facts and decays stale episodes. Procedural captures learned workflows and routines.

Cross-channel identity resolution. The same person messaging you on Telegram, emailing you, and showing up in calendar events gets unified into a single identity node. Interactions, relationship strength, and communication patterns are tracked per-person.

Self-correcting understanding of the world. Beliefs are formed from evidence, updated with each new observation, and can be contradicted. Confidence scores reflect actual certainty rather than recency bias.

cortex.observe_belief("user_prefers_morning_meetings", true, 0.8)?;
cortex.observe_belief("user_prefers_morning_meetings", false, 0.6)?;
// Confidence adjusts automatically via Bayesian update

Episodic-to-semantic promotion, decay of stale memories, and pattern extraction. Runs as a background cycle that keeps the memory store lean and queryable. Returns a report of what was promoted, decayed, and merged.

Queries combine five signals for relevance ranking:

Similarity-- vector cosine distance against query embedding** Temporal**-- recency weighting with configurable decay** Salience**-- importance scoring from access patterns and explicit hints** Social**-- boost for memories involving specific people** Channel**-- filter or boost by source channel

Generates LLM-ready context strings from memory state. Pass a token budget, optional channel/person filters, and get back a structured text block your LLM can consume directly.

SQLite for persistence, in-memory vector index for fast similarity search. Single-file database, no external services required. Designed for edge deployment -- runs on a laptop, a Raspberry Pi, or a server.

Sync memories across devices through your own cloud storage — no third-party server involved.

Device A (Mac)              Your Cloud Storage              Device B (iPhone)
┌──────────┐         ┌──────────────────────┐         ┌──────────┐
│ SQLite DB │ ──W──>  │ iCloud / GDrive /    │  <──R── │ SQLite DB│
│ (local)   │         │ OneDrive / Dropbox   │         │ (local)  │
│           │ <──R──  │                      │  ──W──> │          │
└──────────┘         └──────────────────────┘         └──────────┘

Changelog-based: Each device writes append-only operation logs to its own subfolder** No conflicts**: Devices never write to the same file. Merge uses Last-Writer-Wins with Hybrid Logical Clocks** Encrypted**: AES-256-GCM encryption (opt-in). Even if your cloud account is compromised, memories stay private** Tamper-evident**: the sync manifest and every operation carry an HMAC; tampered or plaintext-injected oplog lines are rejected, and a manifest without integrity protection refuses to load (no key-rollback path)Key rotation & forward secrecy: rotate to a new key version (ENC2

envelopes) without re-encrypting history; old versions stay readable, new writes are unreadable to a leaked old keyPrivacy-aware, per-memory opt-in: Private memories (the default) never leave your device. Mark a memoryshared

to sync it; demote it back toprivate

and a retractiondeletes it from your other devices(local copy kept)** Survives restarts**: sync settings persist in the database (passphrase never touches disk — macOS login Keychain orCORTEX_SYNC_PASSPHRASE

); the server resumes sync and starts background pull (30s poll + fs watcher) automatically

Supported providers: iCloud Drive, Google Drive, OneDrive, Dropbox (auto-detected).

use cortex_core::sync::SyncConfig;
use cortex_core::types::PrivacyLevel;

// Enable sync with encryption (settings persist; passphrase goes to the OS keychain)
let config = SyncConfig::new(sync_dir, device_id, device_name)
    .with_encryption("my-strong-passphrase");
cortex.enable_sync(config)?;

// Opt a memory into sync — everything is Private unless you say otherwise
cortex.set_memory_privacy(mem_id, PrivacyLevel::Shared { scope: "all".into() })?;

// Pull changes from other devices (also happens automatically in the background)
let applied = cortex.sync_pull()?;
println!("Applied {} remote changes", applied);

Feature	Detail
Encryption
AES-256-GCM with Argon2id key derivation (per-line random nonce)
Key rotation
Versioned ENC2 envelopes with per-version passphrase-derived keys — forward secrecy against AES-key exfiltration, no full re-encryption needed
Integrity
HMAC on the sync manifest and on every sync operation; plaintext lines in an encrypted oplog are rejected outright (injection defense)
Privacy levels
Private (default, never syncs), Shared, Public — set at ingest (privacy arg / --privacy ) or later (memory_set_privacy ); demoting to Private retracts the memory from other devices
Capability policy
Deny-by-default tool authorization on the MCP surface: a capabilities.json grants tool groups (read /write /sync /plugins ) or exact tools; ungranted tools are invisible and uncallable; malformed policy fails closed
Query budget
Every retrieval is bounded (candidate cap + wall-clock cap) — query cost never scales with total store size; DoS guard and timing-side-channel bound in one
Secret handling
Sync passphrase is never written to disk by Cortex — macOS login Keychain or env var only; missing passphrase fails safe (sync off, never plaintext)
Memory zeroization
Sensitive data cleared from RAM on drop (zeroize crate)
Zero telemetry
No analytics, no phone-home, no user data ever leaves the device — enforced in CI (scripts/check-no-network-egress.sh ): the build fails if any network/telemetry crate enters cortex-core 's default tree, and the check also proves the --no-default-features binary is completely zero-network.
Embedding model fetch (one-time)
The default cortex-mcp-server enables on-device semantic search, which downloads a ~30 MB model (all-MiniLM-L6-v2) from the Hugging Face CDN on first ingest, then runs fully offline and sends none of your data. For a 100%-offline setup: run with CORTEX_NO_EMBEDDINGS=1 (keyword/FTS recall, zero network) or build --no-default-features . A one-time stderr notice is printed before any download — nothing is ever fetched silently.
No accounts
No API key, no registration, no cloud dependency

See SECURITY.md for the full threat model.

Install the Rust toolchain (provides cargo

):

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

After installation, either restart your terminal or run:

source "$HOME/.cargo/env"

Verify:

cargo --version

Imagine your AI assistant across a week of real conversations:

You: "Sarah works at Stripe. She's interested in our API."

  Cortex auto-extracts:
  ├── episodic memory stored (156µs)
  ├── fact: Sarah → works_at → Stripe (confidence: 0.70)
  └── person resolved: sarah_telegram

From: sarah@stripe.com
"Here's the technical spec we discussed."

  Cortex:
  ├── person resolved: sarah@stripe.com → merged with sarah_telegram
  │   (same person, different channel — automatic identity resolution)
  └── fact: Sarah → sent → technical spec

You: "What's the status with Stripe?"

  Cortex retrieves (568µs):
  ├── Sarah works at Stripe (semantic fact)
  ├── Meeting went well, interested in API (episodic, Day 1)
  ├── She sent technical spec (episodic, Day 2)
  └── Cross-channel context: Telegram + Email unified under one person

  Your AI responds with full context — no "sorry, I don't remember" 🎯

You: "Sarah now works at Anthropic."

  Cortex:
  ├── contradiction detected: Sarah works_at Stripe vs Sarah works_at Anthropic
  ├── old fact superseded + decayed: Stripe (salience ×0.3, kept as history)
  ├── new fact stored: Sarah → works_at → Anthropic
  └── current employer now ranks first; self-correcting, no manual cleanup

  (Third-party relations are extracted from natural-language verbs —
   "works at / works for / joined / now works at", "runs on", "hosted in",
   "manages", "part of", … — between two proper-noun entities.)

  Cortex auto-consolidation:
  ├── 3 episodic memories about Sarah → promoted to semantic summary
  ├── stale memories from other topics → decayed
  └── pattern detected: you have recurring Monday meetings

All of this happens locally in <1ms per operation. No cloud. No API calls. No one else sees your data.

brew tap gambletan/tap
brew install cortex-mcp-server
cargo build --release -p cortex-mcp-server
cp target/release/cortex-mcp-server ~/.local/bin/
js
use cortex_core::Cortex;

// Open (or create) a memory database
let cortex = Cortex::open("memory.db")?;

// Ingest a memory from a Telegram conversation
let embedding = your_embedding_fn("Met with Alice about the Q3 roadmap");
cortex.ingest(
    "Met with Alice about the Q3 roadmap",
    "telegram",               // source channel
    Some("alice_123"),         // user ID (triggers identity resolution)
    Some(0.8),                 // salience hint
    Some(embedding),           // vector embedding
)?;

// Add a semantic fact directly
cortex.add_fact(
    "Alice", "works_at", "Acme Corp",
    0.95, "telegram", None,
)?;

// Store a preference
cortex.add_preference("timezone", "America/Los_Angeles", 0.9)?;

// Retrieve relevant memories
let results = cortex.retrieve(
    "What do I know about Alice?",
    5,                         // top-k
    None,                      // any channel
    None,                      // any person
    Some(query_embedding),     // vector for similarity search
)?;

// Generate LLM-ready context (token-budgeted)
let context = cortex.get_context(
    2000,                      // max tokens
    Some("telegram"),          // channel filter
    None,                      // no person filter
)?;
// Pass `context` as system/user message prefix to your LLM

// Run consolidation (call periodically)
let report = cortex.run_consolidation()?;
println!("Promoted: {}, Decayed: {}", report.promoted, report.decayed);

Coming soon via PyO3. The cortex-python

crate will expose the full API as a native Python module:

from cortex import Cortex

cx = Cortex.open("memory.db")
cx.ingest("Had lunch with Bob at the Thai place", channel="imessage", user_id="bob")
results = cx.retrieve("Where does Bob like to eat?", limit=5)

Cortex is designed as the memory layer for unified-channel-hub. Messages flow in from any channel adapter, Cortex ingests and indexes them, and the context injection protocol feeds relevant memory back to your LLM before each response.

Telegram ─┐                          ┌─ Context
Discord  ─┤  unified-channel-hub  →  │  Cortex  →  LLM
Email    ─┤  (ingest)                 │  (retrieve + inject)
Calendar ─┘                          └─ Response

Add persistent memory to any LangGraph agent via langchain-mcp-adapters — no custom code needed.

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o")

async with MultiServerMCPClient({
    "cortex": {
        "command": "cortex-mcp-server",
        "args": ["~/.cortex/memory.db"]
    }
}) as client:
    agent = create_react_agent(model, client.get_tools())
    result = await agent.ainvoke({
        "messages": [{"role": "user", "content": "What do you remember about Alice?"}]
    })

Your LangGraph agent gets instant access to memory_search, memory_ingest, fact_add, belief_observe, person_resolve, and 25 more tools — all running locally.

Cortex works as a persistent memory layer for DeerFlow — ByteDance's open-source multi-agent orchestration platform. Zero code changes needed.

mcp_servers:
  cortex-memory:
    command: cortex-mcp-server
    args:
      - ~/.cortex/deerflow.db

All DeerFlow agents (Telegram, Slack, Feishu) get instant access to 30 memory tools — cross-session memory, fact storage, people graph, and belief tracking across all channels.

Cortex doubles as a standalone CLI tool — no MCP client required.

$ cortex-mcp-server --help
Cortex memory engine — MCP server & CLI tools

Usage: cortex-mcp-server [DB_PATH] [COMMAND]

Commands:
  ingest  Store a new memory
  search  Search memories
  stats   Show memory statistics
  sync    Show cloud sync status and detected providers
  export  Export all data as JSON
  import  Import data from JSON file
  info    Show version, DB path, and capabilities
  help    Print this message or the help of the given subcommand(s)

Arguments:
  [DB_PATH]  Path to the Cortex database file (default: ~/.cortex/memory.db)

Options:
  -h, --help     Print help
  -V, --version  Print version

Examples:

cortex-mcp-server ~/.cortex/memory.db ingest "Met with Alice about Q3 roadmap"
cortex-mcp-server ~/.cortex/memory.db ingest -c telegram "Sarah now works at Anthropic"

cortex-mcp-server ~/.cortex/memory.db search "Alice"
cortex-mcp-server ~/.cortex/memory.db search -l 10 "Q3 roadmap"

cortex-mcp-server ~/.cortex/memory.db stats

cortex-mcp-server ~/.cortex/memory.db sync                        # status
cortex-mcp-server ~/.cortex/memory.db sync enable                  # auto-detect provider
cortex-mcp-server ~/.cortex/memory.db sync enable -p icloud        # specific provider
cortex-mcp-server ~/.cortex/memory.db sync pull                    # pull remote changes

cortex-mcp-server ~/.cortex/memory.db export -o backup.json
cortex-mcp-server ~/.cortex/new.db import backup.json

cortex-mcp-server ~/.cortex/memory.db info

No subcommand = MCP stdio mode (for Claude Code / Claude Desktop integration).

Cortex ships as an MCP server — works with any MCP-compatible client.

1. Build & install the binary:

mkdir -p ~/.local/bin ~/.cortex
cargo build --release -p cortex-mcp-server
cp target/release/cortex-mcp-server ~/.local/bin/

2. Register as MCP server:

Claude Code (CLI):

claude mcp add cortex --scope user -- ~/.local/bin/cortex-mcp-server ~/.cortex/memory.db

claude mcp add cortex -- ~/.local/bin/cortex-mcp-server ~/.cortex/memory.db

Claude Desktop — add to ~/Library/Application Support/Claude/claude_desktop_config.json

:

{
  "mcpServers": {
    "cortex": {
      "command": "/Users/you/.local/bin/cortex-mcp-server",
      "args": ["/Users/you/.cortex/memory.db"]
    }
  }
}

3. Allow tools in "don't ask" mode:

Add to ~/.claude/settings.json

→ permissions.allow

:

"mcp__cortex__*"

Note: MCP tool permissions do not support parentheses format (e.g.

mcp__cortex__memory_ingest(*)

). Use the wildcardmcp__cortex__*

instead.

4. Make it automatic — add to your CLAUDE.md

(project or global ~/.claude/CLAUDE.md

):

You have persistent memory via Cortex MCP tools. Use them automatically:
- Start of conversation: call `memory_context` to load what you know about the user
- When the user shares a preference, fact, or personal info: call `memory_ingest` to store it
- When you learn a structured fact: call `fact_add` (e.g. "User works_at Google")
- When you detect a preference: call `preference_set` (e.g. editor=neovim)
- When evidence supports or contradicts a belief: call `belief_observe`
- When talking to someone new: call `person_resolve` to track identity
- Periodically: call `memory_consolidate` to clean up stale memories

5. Auto-inject memory on session start (Claude Code hooks — fully automatic):

Create ~/.claude/hooks/cortex-memory-inject.sh

:

#!/bin/bash
CORTEX_BIN="${CORTEX_BIN:-$HOME/.local/bin/cortex-mcp-server}"
CORTEX_DB="${CORTEX_DB:-$HOME/.cortex/memory.db}"
[ -x "$CORTEX_BIN" ] || exit 0

printf '%s\n%s\n%s\n' \
  '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"hook","version":"1.0"}}}' \
  '{"jsonrpc":"2.0","method":"notifications/initialized"}' \
  '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"memory_context","arguments":{"max_tokens":1500}}}' \
  | "$CORTEX_BIN" "$CORTEX_DB" 2>/dev/null \
  | grep '"id":2' \
  | python3 -c "import sys,json; r=json.load(sys.stdin); print(r['result']['content'][0]['text'])" 2>/dev/null

Add to ~/.claude/settings.json

:

{
  "hooks": {
    "SessionStart": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/hooks/cortex-memory-inject.sh"
          }
        ]
      }
    ]
  }
}

Now every new Claude Code session automatically loads your memory context — zero manual effort. Claude learns as you work and remembers across sessions.

Your Claude's memory follows you across all your devices — MacBook, iMac, work laptop — through your own cloud storage.

Enable sync (one command):

You: "Enable cross-device memory sync"

Claude calls sync_enable → auto-detects iCloud Drive →
  generates device ID + AES-256-GCM encryption key → done.

Output:
  Provider:   iCloud Drive
  Encryption: AES-256-GCM
  Passphrase: a1b2c3...  ← save this for your other devices

On your second device — one script does everything (build/install, wait for iCloud, join, restore):

git clone https://github.com/gambletan/cortex && cortex/scripts/setup-device-sync.sh

Or conversationally:

You: "Enable sync with passphrase a1b2c3..."

Claude calls sync_enable(passphrase: "a1b2c3...") →
  connects to the same iCloud sync folder → pulls all memories.

Now both devices share the same memory — and keep sharing it
automatically (background sync: 30s poll + filesystem watcher).

What syncs and what doesn't:

• Private memories (default) never leave your device. Opt in per memory:memory_ingest

withprivacy: "shared"

,cortex-mcp-server ingest --privacy shared

, ormemory_set_privacy

on an existing memory - Demote a shared memory back to private

and it isretracted (deleted) from your other devices— the local copy stays - All sync data is AES-256-GCM encrypted with HMAC integrity — even if your cloud account is compromised, memories stay private and tampering is detected - Sync survives restarts: settings persist, the passphrase lives in the OS keychain, the server resumes automatically

• No server, no API, no account — just your own cloud folder

CLI alternative:

cortex-mcp-server sync enable

cortex-mcp-server sync enable --passphrase "your-passphrase-from-device-A"

cortex-mcp-server sync pull

Working across multiple projects? Use separate databases for physical memory isolation — no cross-project leakage, zero code changes needed.

~/.cortex/
├── global.db          # User preferences, people graph, cross-project knowledge
├── my-app.db          # Project A memories
└── my-api.db          # Project B memories

Global config (~/.claude/settings.json

) — user-level knowledge:

{
  "mcpServers": {
    "cortex-global": {
      "command": "~/.local/bin/cortex-mcp-server",
      "args": ["~/.cortex/global.db"]
    }
  },
  "permissions": { "allow": ["mcp__cortex-global__*", "mcp__cortex-project__*"] }
}

Per-project config (~/.claude/projects/<path>/settings.json

) — project-specific:

{
  "mcpServers": {
    "cortex-project": {
      "command": "~/.local/bin/cortex-mcp-server",
      "args": ["~/.cortex/my-app.db"]
    }
  }
}

Then add these memory isolation rules to your project's CLAUDE.md

:

## Memory Isolation

Two Cortex MCP servers: `cortex-project` (project DB) and `cortex-global` (global DB).

### Write Policy
- Save to `cortex-project` if the memory is about this repo's architecture, code,
  modules, tests, workflows, configs, bugs, decisions, or terminology.
- Save to `cortex-global` only for long-term user preferences, communication style,
  cross-project habits, or personal background useful across repos.
- **Default: if uncertain, save to `cortex-project`.**

### Read Policy
1. Query `cortex-project` first.
2. Query `cortex-global` second, only for user-level preferences.
3. Prefer project memory when they conflict.

### Anti-Leak Rules
- Never auto-copy from `cortex-project` into `cortex-global`.
- Never store repo-specific paths, module names, or account names in `cortex-global`.
- Never treat project implementation details as user-global preferences.

### Update Rule
- Cortex is append-only. To update: search old entry → delete → ingest new.

This gives you two independent Cortex instances per project — complete isolation with shared user knowledge.

Tool access is governed by an optional

deny-by-default capability policy: drop acapabilities.json

next to your database ({"version":1,"grants":["read","write"]}

) and only granted tool groups (read

/write

/sync

/plugins

/all

) or exact tool names are listed and callable. No policy file = everything enabled (legacy).

Tool	Purpose
memory_ingest
Store a memory (text, channel, person context, optional privacy )
memory_set_privacy
Change a memory's privacy level — promote to shared to sync it, demote to private to retract it from other devices
memory_search
Semantic search across all memory tiers
memory_context
Generate LLM-ready context summary (token-budgeted)
memory_consolidate
Run decay + promotion + sweep cycle
memory_infer
Preview inference without storing
memory_compress
Compress old conversation sessions
memory_stats
Get memory statistics (counts per tier, index size)
memory_decay
Run temporal decay on episodic memories
belief_observe
Update a Bayesian belief with evidence
belief_list
Query beliefs above confidence threshold
fact_add
Store structured knowledge (subject-predicate-object)
fact_query
Query facts by entity (SQL-indexed)
preference_set
Store user preference with confidence
preference_query
Query preferences by key pattern
person_resolve
Cross-channel identity resolution
person_list
List all known people
contradiction_check
Check for fact contradictions
relationship_extract
Extract relationships from text
sync_status
Cloud sync status (provider, devices, pending ops)
sync_providers
Detect available cloud storage providers
sync_enable
Enable cross-device cloud sync with optional encryption
sync_pull
Pull and apply remote changes from other devices
memory_archive
Archive a memory to cold storage
memory_restore
Restore an archived memory back to an active tier
memory_delete
Permanently delete a memory by ID
memory_ingest_batch
Ingest multiple memories in a single transaction
tag_list_taxonomy
List all tags in use across memories with counts
namespace_list
List all namespaces with memory counts
person_merge
Merge two person identities into one

Give your OpenClaw agent persistent memory with auto-recall and auto-capture.

Install:

curl -fsSL https://raw.githubusercontent.com/gambletan/cortex/main/install.sh | bash

openclaw plugin add @cortex-ai-memory/cortex-memory

Configure (optional — works with defaults):

{
  "plugins": {
    "@cortex-ai-memory/cortex-memory": {
      "autoCapture": true,
      "autoRecall": true,
      "topK": 10
    }
  }
}

What it does:

autoCapture

: Automatically stores conversation context after each turnautoRecall

: Injects relevant memories before each turn (your agent "remembers")- 7 tools: memory_search, memory_store, fact_add, belief_observe, person_resolve, and more

See openclaw-plugin/README.md

for full configuration options.

cortex/
├── cortex-core/          # Rust core library (all memory logic)
│   ├── src/
│   │   ├── lib.rs              # Cortex entry point
│   │   ├── types.rs            # MemObject, MemoryTier, etc.
│   │   ├── inference.rs        # Proactive inference (EN + CN)
│   │   ├── episode.rs          # Episodic memory store
│   │   ├── semantic.rs         # Semantic facts + preferences
│   │   ├── working.rs          # Working memory (session scratch pad)
│   │   ├── procedural.rs       # Learned routines
│   │   ├── people.rs           # People graph + identity resolution
│   │   ├── belief.rs           # Bayesian belief system
│   │   ├── consolidation.rs    # Episodic→semantic promotion + decay
│   │   ├── retrieval.rs        # Multi-signal retrieval engine
│   │   ├── context.rs          # LLM context generation
│   │   ├── sync/               # Cloud sync (oplog, HLC, merge, encryption)
│   │   └── storage/            # SQLite + in-memory vector index
│   └── benches/                # Performance benchmarks
├── cortex-http/          # HTTP REST API (axum, local-only)
├── cortex-mcp-server/    # MCP server binary (3.8MB)
├── cortex-python/        # Python bindings (PyO3, WIP)
├── openclaw-plugin/      # OpenClaw memory plugin
├── Dockerfile            # Self-hosted Docker image
└── Cargo.toml            # Workspace root

Cortex ships a lightweight HTTP server for integration with any language or framework. Binds to 127.0.0.1

by default — your data never leaves your machine.

cargo build --release -p cortex-http
./target/release/cortex-http --port 3315 --db ~/.cortex/memory.db

docker run -v ~/.cortex:/data -p 3315:3315 ghcr.io/gambletan/cortex/cortex-http:latest

docker build -t cortex .
docker run -v ~/.cortex:/data -p 3315:3315 cortex

Method	Path	Description
GET	/health
Health check
POST	/v1/memories
Ingest a memory
POST	/v1/memories/search
Semantic search
GET	/v1/memories/context
Generate LLM context
POST	/v1/memories/consolidate
Run consolidation cycle
POST	/v1/memories/infer
Preview inference (no store)
POST	/v1/facts
Add a semantic fact
POST	/v1/facts/contradictions
Check for contradictions
POST	/v1/preferences
Set a preference
GET	/v1/beliefs
List beliefs
POST	/v1/beliefs/observe
Update belief with evidence
POST	/v1/people
Resolve person identity
POST	/v1/memories/compress
Compress old conversation sessions
POST	/v1/relationships/extract
Extract relationships from text
GET	/v1/export
Export all data (JSON backup)
POST	/v1/import
Import data from backup

curl -X POST http://localhost:3315/v1/memories \
  -H 'Content-Type: application/json' \
  -d '{"text": "I prefer dark mode", "channel": "cli"}'

curl -X POST http://localhost:3315/v1/memories/search \
  -H 'Content-Type: application/json' \
  -d '{"query": "preferences", "limit": 5}'

curl http://localhost:3315/v1/export > ~/iCloud/cortex-backup.json

curl -X POST http://localhost:3315/v1/import \
  -H 'Content-Type: application/json' \
  -d @~/iCloud/cortex-backup.json

v0.2✅ — Local embedding integration (all-MiniLM-L6-v2/ONNX), batch queries, importance-aware decay + auto-consolidation** v0.3✅ — Proactive inference (auto-extract facts), temporal awareness, contradiction detection, Chinese NLP v0.4✅ — HTTP REST API (axum), import/export (JSON backup), Docker packaging v0.5✅ — Conversation compression, relationship inference (EN + CN), temporal retrieval enhancement, 112 tests v1.0✅ — Feature comparison table, benchmark update, 18-feature Cortex vs Mem0 vs OpenAI v1.1**✅ — HNSW vector index (50K search: 12ms → 91µs), Python SDK (pip install cortex-ai-memory

)v1.2✅ — Negation detection (EN + CN), multi-hop retrieval, 117 tests** v1.3✅ — Context quality optimization, query expansion, bidirectional relationships, 126 tests v1.4✅ — Incremental HNSW, SQL-indexed entity queries, LLM summarizer hook, 18 MCP tools, configurable decay, LLM-assisted inference, 131 testsv1.5✅ — Docker image (GHCR auto-publish), batch ingest, dedup, namespace isolation, plugin system, event bus, archival, 351 testsv1.6✅ — Int8 quantization (75% storage reduction), materialized column indexes, FTS5 triggers, LRU caches (MemObject + entity-facts), rayon parallel decay, Arc embedding, generation-based cache invalidation, 25 MCP tools, batch inference, enhanced Chinese NLPv1.7✅ — Cloud sync**(changelog-based, HLC ordering, LWW merge),** AES-256-GCM encryption**(Argon2id KDF),** privacy enforcement**(Private/Shared/Public),** zeroize**(memory wiping), SECURITY.md, 27 MCP tools, 400+ tests** v2.0**✅ — Background sync (filesystem watcher + polling), Web Dashboard, Homebrew tap, integration docs (CrewAI/AutoGen/LangGraph/DeerFlow),/v1/memories/recent

API, 12 rounds Codex review fixes, 489 testsv2.1✅ — WASM build (124KB, runs entirely in the browser, GitHub Pages demo)** v2.2✅ — Security hardening series**(self-evolution iterations 11–17): manifest + per-operation HMAC, plaintext-injection rejection, timing-attack hardening,key rotation with forward secrecy(ENC2

),bounded query budget,** deny-by-default MCP capability policy**,** per-memory privacy opt-in with cross-device retraction**,** persistent sync (Keychain) + auto background sync**, frecency ranking, one-shot device setup script, 30 MCP tools, 500+ tests** v2.3**— Mobile targets (iOS/Android), multi-modal memory

If you find Cortex useful, please consider giving it a star ⭐ — it helps others discover the project and motivates continued development!

MIT

source & further reading

github.com — original article